title: Artificial neural networks assessing adolescent idiopathic scoliosis: comparison with Lenke classification.
abstract: BACKGROUND
  Variability in classifying and selecting levels of fusion in adolescent idiopathic scoliosis (AIS) has been repeatedly documented. Several computer algorithms have been used to classify AIS based on the geometrical features, but none have attempted to analyze its treatment patterns.
  
  
  OBJECTIVE
  To use self-organizing maps (SOM), a kind of artificial neural networks, to reliably classify AIS cases from a large database. To analyze surgeon's treatment pattern in selecting curve regions to fuse in AIS using Lenke classification and SOM.
  
  
  METHODS
  This is a technical concept article on the possibility and benefits of using neural networks to classify AIS and a retrospective analysis of AIS curve regions selected for fusion.
  
  
  METHODS
  A total of 1,776 patients surgically treated for AIS were prospectively enrolled in a multicentric database. Cobb angles were measured on AIS patient spine radiographies, and patients were classified according to Lenke classification.
  
  
  METHODS
  For each patient in the database, surgical approach and levels of fusion selected by the treating surgeon were recorded.
  
  
  METHODS
  A Kohonen SOM was generated using 1,776 surgically treated AIS cases. The quality of the SOM was tested using topological error. Percentages of prediction of fusion based on Lenke classification for each patient in the database and for each node in the SOM were calculated. Lenke curve types, treatment pattern, and kappa statistics for agreement between fusion realized and fusion recommended by Lenke classification were plotted on each node of the map.
  
  
  RESULTS
  The topographic error for the SOM generated was 0.02, which demonstrates high accuracy. The SOM differentiates clear clusters of curve type nodes on the map. The SOM also shows epicenters for main thoracic, double thoracic, and thoracolumbar/lumbar curve types and transition zones between clusters. When cases are taken individually, Lenke classification predicted curve regions fused by the surgeon in 46% of cases. When those cases are reorganized by the SOM into nodes, Lenke classification predicted the curve regions to fuse in 82% of the nodes. Agreement with Lenke classification principles was high in epicenters for curve types 1, 2, and 5, moderate in cluster for curve types 3, 4, and 6, and low in transition zones between curve types.
  
  
  CONCLUSIONS
  An AIS SOM with high accuracy was successfully generated. Lenke classification principles are followed in 46% of the cases but in 82% of the nodes on the SOM. The SOM highlights the tendency of surgeons to follow Lenke classification principles for similar curves on the SOM. Self-organizing map classification of AIS could be valuable to surgeons because it bypasses the limitations imposed by rigid classification such as cutoff values on Cobb angle to define curve types. It can extract similar cases from large databases to analyze and guide treatment.
authors.affiliation: Research Center, Sainte-Justine University Hospital Center, 3175 Chemin de la Côte-Sainte-Catherine, Montreal, Quebec, Canada H3T 1C5; Division of Orthopaedic Surgery, Department of Surgery, The Ottawa Hospital, University of Ottawa, Civic Campus, 1053 Carling Ave., Ottawa, Ontario, Canada K2A 3C8. Electronic address: philippe.phan@gmail.com.
authors.forename: Philippe
authors.initials: P
authors.lastname: Phan
authors.forename: Neila
authors.initials: N
authors.lastname: Mezghani
authors.forename: Eugene K
authors.initials: EK
authors.lastname: Wai
authors.forename: Jacques
authors.initials: J
authors.lastname: de Guise
authors.forename: Hubert
authors.initials: H
authors.lastname: Labelle
mesh_terms: D000293:Adolescent; D000465:Algorithms; D016208:Databases, Factual; D005260:Female; D006801:Humans; D008159:Lumbar Vertebrae / Q000601:surgery; D008297:Male; D016571:Neural Networks, Computer; D011446:Prospective Studies; D012600:Scoliosis / Q000145:classification* / Q000175:diagnosis* / Q000601:surgery; D012984:Software; D013123:Spinal Fusion; D013904:Thoracic Vertebrae / Q000601:surgery; D016896:Treatment Outcome
keywords: Adolescent idiopathic scoliosis; Kohonen self-organizing maps; Lenke classification; Neural networks; Surgical treatment